Extended Directed Fuzzy Social Network Analysis: A framework and application to curriculum networks in Chinese vocational education
Borui Zuo, Keqi Shang, Jie Zhang, Manyu Peng, Zhiming Zhu
TL;DR
This paper introduces a new framework for analyzing fuzzy social networks, applied to curriculum networks in Chinese vocational education to identify core courses and highlight differences between majors.
Contribution
The paper proposes the Extended Directed Fuzzy Social Network Analysis Framework (EFDSNAF) and introduces the Total Fuzzy Intensity of Path (TFIP) for improved network analysis.
Findings
EFDSNAF effectively identifies core courses in vocational education curriculum networks.
The framework captures essential disciplinary differences between two urban rail transit majors.
Optimized fuzzy centrality measures demonstrate improved efficacy in analyzing network relationships.
Abstract
Due to the differences in node types and the diversity of network relationships, Fuzzy Social Network Analysis (FSNA) needs to specifically address the issues of network heterogeneity and relationship ambiguity. To address this challenge, we propose a new analytical framework called Extended Directed Fuzzy Social Network Analysis Framework (EFDSNAF), which establishes the Typical Connections to assist in evaluating the fuzzy network. Meanwhile, in the area of fuzzy centrality measures, we enhance the variability of the Fuzzy Intensity of Path and propose the term “Total Fuzzy Intensity of Path” (TFIP), considering the distinct characteristics of different networks may lead to variations in path intensity expressions and differences in closeness relationships. Based on this, we optimize the computational methods for fuzzy betweenness centrality and fuzzy closeness centrality, with the…
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Taxonomy
TopicsComplex Network Analysis Techniques · Online Learning and Analytics · Innovative Teaching and Learning Methods